%0 Journal Article
%J Complexity
%D 2019
%T A Multilayer Structure Facilitates the Production of Antifragile Systems in Boolean Network Models
%A Kim, Hyobin
%A Pineda, Omar K.
%A Gershenson, Carlos
%X Antifragility is a property from which systems are able to resist stress and furthermore benefit from it. Even though antifragile dynamics is found in various real-world complex systems where multiple subsystems interact with each other, the attribute has not been quantitatively explored yet in those complex systems which can be regarded as multilayer networks. Here we study how the multilayer structure affects the antifragility of the whole system. By comparing single-layer and multilayer Boolean networks based on our recently proposed antifragility measure, we found that the multilayer structure facilitated the production of antifragile systems. Our measure and findings will be useful for various applications such as exploring properties of biological systems with multilayer structures and creating more antifragile engineered systems.
%B Complexity
%V 2019
%P 11
%G eng
%U https://doi.org/10.1155/2019/2783217
%9 10.1155/2019/2783217
%R 10.1155/2019/2783217
%0 Journal Article
%J Entropy
%D 2016
%T Measuring the Complexity of Continuous Distributions
%A Santamaría-Bonfil, Guillermo
%A Fernández, Nelson
%A Gershenson, Carlos
%X We extend previously proposed measures of complexity, emergence, and self-organization to continuous distributions using differential entropy. Given that the measures were based on Shannon's information, the novel continuous complexity measures describe how a system's predictability changes in terms of the probability distribution parameters. This allows us to calculate the complexity of phenomena for which distributions are known. We find that a broad range of common parameters found in Gaussian and scale-free distributions present high complexity values. We also explore the relationship between our measure of complexity and information adaptation.
%B Entropy
%V 18
%P 72
%G eng
%U http://www.mdpi.com/1099-4300/18/3/72
%R 10.3390/e18030072
%0 Journal Article
%J Peer-to-Peer Networking and Applications
%D 2015
%T Measuring the complexity of adaptive peer-to-peer systems
%A Amoretti, Michele
%A Gershenson, Carlos
%K Adaptive peer-to-peer system
%K Complexity
%K Evolution
%K Information theory
%X To improve the efficiency of peer-to-peer (P2P) systems while adapting to changing environmental conditions, static peer-to-peer protocols can be replaced by adaptive plans. The resulting systems are inherently complex, which makes their development and characterization a challenge for traditional methods. Here we propose the design and analysis of adaptive P2P systems using measures of complexity, emergence, self-organization, and homeostasis based on information theory. These measures allow the evaluation of adaptive P2P systems and thus can be used to guide their design. We evaluate the proposal with a P2P computing system provided with adaptation mechanisms. We show the evolution of the system with static and also changing workload, using different fitness functions. When the adaptive plan forces the system to converge to a predefined performance level, the nodes may result in highly unstable configurations, which correspond to a high variance in time of the measured complexity. Conversely, if the adaptive plan is less ``aggressive'', the system may be more stable, but the optimal performance may not be achieved.
%B Peer-to-Peer Networking and Applications
%P 1-16
%@ 1936-6442
%G eng
%U http://dx.doi.org/10.1007/s12083-015-0385-4
%R 10.1007/s12083-015-0385-4
%0 Book Section
%B Advances in Computational Biology
%D 2014
%T Measuring Complexity in an Aquatic Ecosystem
%A Fernández, Nelson
%A Gershenson, Carlos
%E Castillo, Luis F.
%E Cristancho, Marco
%E Isaza, Gustavo
%E Pinzón, Andrés
%E Corchado Rodríguez, Juan Manuel
%X We apply formal measures of emergence, self-organization, homeostasis, autopoiesis and complexity to an aquatic ecosystem; in particular to the physiochemical component of an Arctic lake. These measures are based on information theory. Variables with an homogeneous distribution have higher values of emergence, while variables with a more heterogeneous distribution have a higher self-organization. Variables with a high complexity reflect a balance between change (emergence) and regularity/order (self-organization). In addition, homeostasis values coincide with the variation of the winter and summer seasons. Autopoiesis values show a higher degree of independence of biological components over their environment. Our approach shows how the ecological dynamics can be described in terms of information.
%B Advances in Computational Biology
%S Advances in Intelligent Systems and Computing
%I Springer
%V 232
%P 83-89
%G eng
%U http://arxiv.org/abs/1305.5413
%R 10.1007/978-3-319-01568-2_12
%0 Journal Article
%J Artificial Life
%D 2010
%T Mechanical Love. Phie Ambo. (2009, Icarus Films.) $390, 52 min.
%A Gershenson, Carlos
%A Meza, Iván V.
%A Avilés, Héctor
%A Pineda, Luis A.
%B Artificial Life
%V 16
%P 269-270
%G eng
%U http://www.mitpressjournals.org/doi/abs/10.1162/artl_r_00004
%R 10.1162/artl_r_00004